An Improved Photovoltaic Power Forecasting Model With the Assistance of Aerosol Index Data | IEEE Journals & Magazine | IEEE Xplore

An Improved Photovoltaic Power Forecasting Model With the Assistance of Aerosol Index Data


Abstract:

Due to the intermittency and randomness of solar photovoltaic (PV) power, it is difficult for system operators to dispatch PV power stations. In order to find a precise e...Show More

Abstract:

Due to the intermittency and randomness of solar photovoltaic (PV) power, it is difficult for system operators to dispatch PV power stations. In order to find a precise expectation for day-ahead PV power generation, conventional models have taken into consideration the temperature, humidity, and wind speed data for forecasting, but these predictions were always not accurate enough under extreme weather conditions. Aerosol index (AI), which indicates the particulate matter in the atmosphere, has been found to have strong linear correlation with solar radiation attenuation, and might have potential influence on the power generated by PV panels. A novel PV power forecasting model is proposed in this paper, considering AI data as an additional input parameter. Based on seasonal weather classification, the back propagation (BP) artificial neural network (ANN) approach is utilized to forecast the next 24-h PV power outputs. The estimated results of the proposed PV power forecasting model coincide well with measurement data, and the proposed model has shown the ability of improving prediction accuracy, compared with conventional methods using ANN.
Published in: IEEE Transactions on Sustainable Energy ( Volume: 6, Issue: 2, April 2015)
Page(s): 434 - 442
Date of Publication: 02 February 2015

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